In recent years, in order to increase the speed of deep learning in which a large amount of data is processed, the use of distributed parallel computation has been attempted. In distributed deep learning using distributed parallel computation, in order to store training data, a high-performance computing (HPC) parallel file system such as Lustre (trademark) may be used.
A self-regulated memory management system that improves performance for memory management is also known (refer to, for example, Japanese Laid-open Patent Publication No. 2004-133934).
In the HPC parallel file system, multiple data nodes may store training data for deep learning in a distributed manner, and the training data may be easily shared between multiple computing nodes that execute the deep learning in parallel. The training data may be accessed as a memory-mapped file by each of the computing nodes. Since a large amount of data may be processed in the HPC parallel file system, it is preferable that data be supplied based on processing performance of the computing nodes.